Flexible employment oriented talent and post intelligent matching system and decision-making method

Abstract

The invention belongs to the technical field of recruitment platforms, and particularly relates to a talent and post intelligent matching decision-making method for flexible employment, and the method comprises the following specific steps: a data fusion and portrait construction stage; an intelligent matching engine stage; an interactive verification and iteration stage; an interactive verification and iteration stage; the invention discloses an intelligent talent and post matching system for flexible employment. A Wisher end is internally provided with an occupational planning analysis system, an AI interview platform and a matching scheduling engine; the Matcher end is internally provided with a candidate matching system and an AI initial interview building system. According to the invention, a talent ecosystem which takes AI and data as dual cores and aims at enabling users is constructed based on a hybrid expert architecture and by fusing high-quality data sets in the human resource field and the digital trade field, so that talents and enterprises are efficiently connected, and the talent value is optimized and the potential energy of the talents is activated.

Description

Technical Field

[0001] This invention belongs to the technical field of recruitment platforms, specifically relating to an intelligent matching system and decision-making method for talent and job positions for flexible employment. Background Technology

[0002] A recruitment platform is an online service platform built using internet technology that connects employers (enterprises) and job seekers, enabling digital management of the entire process, including job posting, resume screening, talent matching, and interviews. Essentially, it is a digital carrier of human resources services, aiming to improve recruitment efficiency and reduce information asymmetry. This paper defines the concept, characteristics, and classification of the knowledge-intensive gig economy, a new economic form. It then analyzes the impact mechanism of artificial intelligence (AI) on this economy using transaction cost theory and signaling theory. The findings reveal that AI promotes the development of the knowledge-intensive gig economy through the dual effects of reducing transaction costs and enhancing signal transmission. The gig economy is a product of the internet age and a new business model emerging under the current sharing economy. Amidst fierce competition in the job market, the number of flexible workers is surging and becoming a trend, while companies, facing difficulties in recruitment and high labor acquisition costs, are also embracing the gig market. Problems with existing technology: Insufficient matching accuracy: Relying on keyword filtering (such as education level, years of work experience) and ignoring implicit skills (such as cross-cultural communication and problem-solving); General-purpose platforms have difficulty understanding the skill differences in the digital trade field (such as the difference in responsibilities between "cross-border e-commerce operations" and "traditional trade operations"). Inefficiency and high cost: HR needs to manually screen a massive number of resumes, with an average processing time of 3-5 minutes per resume, and the cost of bulk recruitment exceeds ten million; multinational companies experience communication delays due to time differences and language barriers, and the average vacancy period for positions is as long as 6 months. Service limitations: Focusing solely on "matching people with jobs," lacking full-cycle services such as career planning and skills enhancement; candidates receive little feedback after submitting their applications, resulting in a poor user experience; and companies lack the ability to dynamically manage their talent pools. Compliance and data risks: Cross-border employment requires compliance with the regulations of multiple countries (such as the EU GDPR and Southeast Asian labor laws), and the cost of human compliance has increased by 40%; the problem of data silos is prominent, and cross-regional systems (such as Workday and local HR systems) cannot work together. Summary of the Invention

[0003] The purpose of this invention is to provide a talent and job intelligent matching system and decision-making method for flexible employment. Based on a multi-agent architecture of hybrid experts, it integrates high-quality datasets from the fields of human resources and digital trade, and builds a talent ecosystem with AI and data as the dual core and user empowerment as the goal. It not only efficiently connects talents and enterprises, but also strives to optimize the value of talents themselves and activate their potential energy.

[0004] The specific technical solution adopted by this invention is as follows: A talent-job intelligent matching decision-making method for flexible employment, the specific steps of which are as follows: Data fusion and profile building stage: Multimodal data collection, analysis of user information to analyze career planning needs, analysis of historical AI interview videos to obtain evaluations; decomposition of job descriptions, job requirements, and corporate salary range; dynamic profile generation, transforming skills, experience, and behavioral preferences into high-dimensional vectors, superimposing psychological assessment results to form a 360° view, generating talent profiles; based on knowledge graphs, associating skill nodes, quantifying implicit needs, and generating job profiles; Intelligent matching engine stage: Through intelligent talent-job precise matching and recommendation, a matching bridge is established between talent resumes and job requirements; one-click matching and scheduling realizes the coupling of talent supply side and thousands of enterprise demand side; job candidate matching is based on skills, experience and job requirements, and enterprises create AI interviewers for different positions to conduct initial interviews with candidates. Interactive verification and iteration phase: AI two-way interactive verification, AI interview platform automatically asks follow-up questions to verify the authenticity of skills; negative feedback-driven iteration, collecting failure cases, including reasons for talent rejection and reasons for company dismissal; fine-tuning BERT semantic understanding layer and LTR weights with new data every week; End-to-end traceability and fairness assurance stage: Use SHAP values ​​to deconstruct matching decisions; add bias removal constraints to the feature extraction layer; A / B testing to compare the differences in interview pass rates for different types of talents.

[0005] The specific process of career planning needs analysis is as follows: Demand analysis and data fusion: After the user inputs their requirements, the NLU model extracts key entities, crawls the high-frequency skill requirements in the job description of the target position, and retrieves the user's historical performance data. MoE in-depth analysis, construction of SWOT matrix, and opportunity prediction; The system generates dynamic paths, compares the user's current skill vector with the target job vector, identifies key gaps, analyzes capability deficiencies, and ultimately generates solutions.

[0006] The specific process of the AI ​​interview and assessment is as follows: Competency binding and question generation: After an enterprise selects competency tags, the system calls the knowledge graph to generate behavioral event interview questions; Initial Questioning and Real-Time Analysis: While candidates are answering questions via video, the system simultaneously performs speech-to-text conversion, sentiment polarity scoring, and micro-expression analysis. Intelligent follow-up questioning and in-depth analysis: If the answer does not mention specific action steps, a follow-up questioning chain is triggered; a decision tree model is used to select the follow-up questioning strategy based on the type of missing keywords; The three-tiered assessment report generation process includes: a surface-level report assessing speech clarity and terminology usage; a mid-level report assessing the authenticity of the case study; a deep-level report assessing resilience; and finally, a radar chart and improvement suggestions.

[0007] The specific process of intelligent talent-job matching and recommendation is as follows: Data input and preprocessing: Users upload resumes, set job preferences, and generate behavioral data; HR publishes job descriptions, sets screening criteria, and generates behavioral data. Profile building and vectorization: The NLP engine parses resumes and job descriptions to extract key information; it combines knowledge graphs to perform semantic enhancement and association expansion; and it generates dynamically updated talent profile vectors and job profile vectors, which are then stored in a vector database. Matching calculation and recommendation generation: The most relevant candidates to the target are quickly retrieved from the vector database using an efficient approximate nearest neighbor search algorithm; the recalled results are finely sorted using a more complex LTR model; finally, the recommendation results are output in order of matching degree, and the reasons for the recommendation are provided. Results Presentation and Feedback Loop: On the talent side, the "Recommended Jobs" section displays a list of matching positions, annotating key matching points and reasons for recommendation, allowing users to apply, save, or ignore. On the enterprise side, the "Recommended Talents" list corresponding to the position or the talent search displays a list of matching candidates, annotating the matching degree, key skill fit, and potential assessment, allowing users to initiate communication, arrange interviews, and record feedback. The system continuously collects explicit and implicit user feedback on the recommendation results, and the feedback data is fed back into the training set for continuous training and optimization of the NLP model, recommendation model, and knowledge graph.

[0008] The matching calculation and recommendation generation are used to trigger the matching calculation when talents browse job postings, companies search for resumes, new job postings are published, new resumes are uploaded, or system scheduled tasks are performed.

[0009] The matching calculation and recommendation generation are comprehensively considered by the recommendation model, taking into account: semantic matching degree based on content, collaborative filtering signals, relevance of knowledge graph reasoning, user personalized preferences and contextual information, and business rules.

[0010] The specific process of one-click matching and scheduling is as follows: One-click application and profile generation: After users upload their resumes, the system automatically extracts key entities; the AI ​​interview module is activated to supplement implicit ability assessment; Intelligent routing and dynamic matching: Demand pool penetration, ranking and classifying matching companies based on job urgency and corporate talent preferences; Two-way negotiation mechanism, the system automatically compromises and recommends overlapping positions; Real-time feedback and closed-loop optimization: Visual display of application status; automatic reduction of recommendation weight for similar positions after a user rejects a certain type of job.

[0011] The specific process of setting up the AI ​​initial interview is as follows: Role and task definition: The rules engine clarifies the interviewer's responsibilities. By inputting job requirements, the system automatically generates an interview logic tree, including a competency model and taboo rules. Multimodal analysis engine integration: speech recognition translates candidate answers in real time, analyzes speech rate and pause frequency to assess stress resistance; computer vision captures micro-expressions through facial key point detection to identify emotional stability; natural language processing combined with BERT variants parses the logic of answers and matches them with a job keyword database; Dynamic knowledge base construction: Integrating psychological models to generate personality suitability reports; accessing industry databases to build differentiated question banks based on job type.

[0012] A talent and job intelligent matching system for flexible employment, including Wiser and Matcher ends: Wiser includes a career planning analysis system, an AI interview platform, and a matching and scheduling engine. The career planning analysis system is based on a hybrid expert model analysis engine, integrating NLU model, dynamic profiling engine and dynamic knowledge base system. By combining user information, it finds a suitable path and realizes career assessment, job analysis, ability assessment, resume optimization and workplace Q&A functions. The AI ​​interview platform is based on the intelligent talent-job precise matching and recommendation function, which establishes a matching bridge between massive talent resumes and job requirements. Based on the interview-assessment function, it combines multimodal analysis and dynamic question bank to conduct multidimensional, comprehensive and objective evaluation. The matching and scheduling engine can deliver to multiple enterprises in one go, relying on dynamic talent profiles and intelligent matching with job requirements; The Matcher client has a candidate matching system and an AI-powered initial interview setup system. The candidate matching system integrates a multimodal job requirement analysis engine, a real-time talent profile matching system, and a hybrid expert decision engine. It matches candidates based on skills, experience, and job requirements, and provides search and filtering functions to help companies find specific types of talent. The AI-powered initial interview setup system is based on a virtual interviewer generation system and an intelligent question bank and question generation system. It helps companies create an interviewer and select the appropriate interviewer to conduct AI interviews for different positions.

[0013] An electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to execute a talent and job intelligent matching decision-making method.

[0014] The technical effects achieved by this invention are as follows: (1) Technology-driven: Deep intelligence in vertical fields Dynamic Knowledge Graph and Semantic Understanding: Construct a dual-domain knowledge graph of "Human Resources - Digital Trade" to analyze the fine-grained relationship between job requirements and talent skills (e.g., "TikTok Operation" needs to be associated with sub-skills such as "Data Analysis" and "Cross-cultural Marketing"). Multimodal assessment and latent ability mining: Combining competency models (such as the iceberg theory and MBTI) with AI interview analysis: Voice / micro-expression recognition: assessing stress resistance and communication logic; Behavioral event analysis: inferring leadership and innovative thinking through project experience descriptions, making the "talent fuel" of the input engine of higher quality and more in line with market demands.

[0015] (2) Full-chain service: from "job matching" to "career growth" Talent side (Wiser) AI Career Partner: Generates a 3-5 year growth plan based on SWOT analysis and Monte Carlo path deduction; One application, thousands of matching companies: Dynamic profile engine matches job requirements across the entire network in real time and pushes highly relevant opportunities; Enterprise side (Matcher) Intelligent talent pool management: RPA automatically synchronizes resumes from multiple channels, and NLP achieves a 98% plagiarism detection accuracy rate; Global compliance engine: Automatically adapts to policies in 120+ countries (such as Vietnam's privacy protection law), reducing risks by 92%.

[0016] (3) Efficiency and cost innovation AI replaces transactional tasks: Resume screening time is reduced from 3 days to 3 hours (for companies with tens of thousands of employees); Cross-border payroll settlement relies on exchange rate prediction models to save on exchange losses; A new paradigm of human-machine collaboration: Utilizing AI dialogue, deep insights, and intelligent interviewing (which can ask questions, delve deeper, identify issues, and evaluate performance), we can deeply deconstruct enterprise needs and penetrate the surface of resumes to assess the deep capabilities of talent.

[0017] (4) Ecological and globalization support Cross-border collaboration platform: integrates multilingual translation, cultural sensitivity identification, and localized payments (such as Southeast Asian e-wallets), shortening the local team building cycle by 40%; Big data on industry talent: Real-time analysis of regional skills gaps (such as the 67% annual increase in live-streaming e-commerce in ASEAN) to guide talent skills upgrading. Attached Figure Description

[0018] Figure 1 This is a composition diagram of the matching system provided in an embodiment of the present invention; Figure 2 This is a flowchart of the matching decision method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the electronic device structure provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe one or more specific embodiments of the invention and does not strictly limit the scope of protection specifically claimed by the invention.

[0020] like Figure 1 As shown, a talent and job intelligent matching system for flexible employment includes a Wiser end and a Matcher end. The Wiser end is equipped with a career planning analysis system, an AI interview platform, and a matching scheduling engine. The Matcher end is equipped with a candidate matching system and an AI initial interview setup system.

[0021] Wiser's career planning analysis system is designed for job seekers. Users input their career planning needs into the Wiser chat interface. Using a SWOT analysis framework that combines user-provided personal information with their internal strengths and external opportunities, the system helps users find suitable career paths. It offers functions such as career assessment, job analysis, competency assessment, resume optimization, and workplace Q&A. Specifically, it incorporates the following technologies: 1. Multi-Agent Collaborative System Architecture (1) Agent division of labor: User Intent Analysis Agent: Based on the Transformer NLU model (such as a variant of BERT), combined with domain knowledge graphs, analyzes key elements in career planning needs (such as target position, skill gaps, and salary expectations). Data collection agent: Securely obtains user authorization information (educational background, work experience, skills certificates) through the OAuth protocol, and crawls publicly available professional data (such as salary levels on recruitment websites and industry trend reports) in real time. Dynamic Profile Agent: Constructs multi-dimensional vector profiles of users (technical stack mastery, soft skills score, industry experience weight), and uses graph neural networks (GNN) to model career path correlations; HR-DT (Human Resources-Digital Trade) dual-domain alignment algorithm: resolves terminological ambiguities (such as the difference between "operations" in traditional trade and e-commerce).

[0022] 2. Hybrid Expert Model (MoE) Analysis Engine (1) Expert pool design: SWOT analysis expert: Based on rule engine and deep learning (LSTM+CRF) technology, outputs a quantitative strengths (S) and weaknesses (W) score matrix; Opportunity Forecasting Expert: Based on time series models (Prophet) and NLP analysis techniques for industry policies, output probability forecasts of industry trends; Job Matching Expert: Based on multimodal contrastive learning (CLIP improved version) technology, outputs job competency matching degree (0-100 points). Risk warning expert: Based on the survival analysis model (Cox proportional hazards model) technology, outputs an assessment of the probability of career transition failure; (2) Gating mechanism: The gating network adopts a lightweight GBDT model and dynamically allocates expert weights according to the input type (e.g., the request for "switching to cross-border e-commerce" will activate the opportunity prediction expert weight to 0.8); at the same time, a meta-learning framework is introduced to automatically update the expert weight distribution weekly based on the new job data to achieve dynamic weight adjustment.

[0023] 3. Dynamic Knowledge Base System (1) Three-tier data architecture: Foundation layer: Obtain a human resources ontology from HR domain corpus and an industry regulatory knowledge graph from digital trade policy; Real-time layer: Obtain job demand heatmaps from recruitment website APIs, as well as regional development indices derived from macroeconomic indicators; User layer: Obtain the implicit capability assessment model based on behavioral tracking points; (2) SHAP value interpretation model: The decision is made using the SHAP value interpretation model, and the "why the Southeast Asian market is recommended" is visualized (e.g., policy dividends account for 35%, language matching degree is 28%).

[0024] 4. Personalized Generation System (1) Resume optimization engine: Based on the controllable text generation of prefix-tuning, combined with A / B testing to verify the effect of improving the resume pass rate; (2) Career path projection: Monte Carlo Tree Search (MCTS) simulates the long-term benefits of different decision paths and visualizes the 3-year / 5-year development roadmap.

[0025] The specific process of career planning needs analysis is as follows: Step 1: Requirements Analysis and Data Fusion The user entered: "I hope to switch from foreign trade order tracking to cross-border live streaming operations, and double my salary within 3 years." NLU model extracts key entities: [Original position = foreign trade merchandiser, target position = cross-border live streaming operation, time limit = 3 years, target = 200% of salary]; Data Agent Linkage: Crawling high-frequency skill requirements from the job description (JD) of the target position (such as TikTok operation, data analysis tools); retrieving users' historical performance data (order conversion rate, customer rating).

[0026] Step 2: MoE In-Depth Analysis Step 2.1: Constructing the SWOT matrix: Advantage (S) calculation example: def calc_strength(user): language_skill = user.get('English IELTS 7.5') * 0.3 # Language weight cross_culture=user.get('Overseas Study Experience') * 0.4 return min(language_skill + cross_culture, 1.0) # Normalization process Step 2.2: Opportunity Prediction: Access the customs export data API to identify the growth rate of live-streaming e-commerce (e.g., 67% annual growth in the ASEAN market); the policy analysis module scans the digital trade-friendly sections of the RCEP terms.

[0027] Step 3: Dynamic Path Generation Step 3.1: Competency Gap Analysis: Compare the user's current skill vector with the target job vector to identify key gaps (such as lack of data analysis skills). Step 3.2: Solution Packaging: Short term: We recommend the Google Analytics Micro Certificate Course (linked to the MOOC API); Mid-term: We suggest participating in internal transfer programs (matching the company's talent pool data); Long term: Plan a management career path (requires an MBA degree).

[0028] Step 4: Real-time feedback optimization Dialogue memory mechanism: By storing historical decisions through Memory Networks, when a user reports "insufficient course time", the system automatically switches to a recommended fragmented learning plan; Multi-round game simulation: Using reinforcement learning (PPO algorithm) to simulate the corporate recruitment decision-making process and predict the probability of an interview invitation after resume revision.

[0029] Wiser's AI interview platform I. Intelligent Talent-Job Matching and Recommendation: Utilizing AI technology, an efficient, accurate, and dynamic matching bridge is established between massive amounts of talent resumes and job requirements. It includes a question bank developed by over 300 interviewers from top companies, providing real-time AI-based simulations to diagnose weaknesses. Going beyond traditional keyword matching, it deeply understands the fit between talent's abilities, experience, potential, and job requirements, achieving personalized and intelligent two-way recommendations. Specific technologies include: 1. Natural Language Processing: (1) Resume / Job Analysis: Use deep learning models (such as BERT, Transformer series) to perform deep semantic understanding on unstructured resume text (skills, project experience, education background, self-evaluation) and job description (responsibilities, requirements, preferences); identify entities (company, position, skills, certificates, projects), relationships, intentions and implicit information; (2) Semantic understanding and vectorization: The parsed text information (talent profile, job profile) is transformed into high-dimensional semantic vectors (Embeddings). These vectors can capture the deep meaning and contextual association of words, phrases and sentences, which is the basis for accurate matching; Sentence-BERT, Doc2Vec or custom pre-trained models are used. (3) Emotional / Potential Analysis: Analyze the tone of voice and description of project achievements in the resume to help assess the candidate's soft skills, professional attitude and development potential.

[0030] 2. Knowledge Graph: (1) Construct a domain knowledge base: Establish a vast knowledge graph covering the digital trade field, including: skill graph (skill name, level, relationship, popularity trend), industry graph (company, product, business model), job graph (job family, responsibility chain, development path), and education / certification graph (the value of schools, majors, and certifications). (2) Relational reasoning: Use knowledge graphs for deep reasoning, for example, identify the strength of the association between “mastering Python data analysis libraries” and the job requirement “data mining engineer”; understand the suitability of “cross-border e-commerce operation experience” for the “overseas market manager” position; and infer the importance weight of “having AWS certification” for the “cloud architect” position.

[0031] 3. Machine Learning and Recommendation Algorithms: (1) Collaborative filtering: Analyze the historical behavioral data of platform users (enterprises and talents) (such as viewing, applying, collecting, interviewing, hiring) to discover patterns such as "what kind of talents similar enterprises like" and "which positions similar talents are interested in"; (2) Content-based recommendation: Core matching engine. Similarity calculation (e.g., cosine similarity) is performed on talent vectors and job vectors in the same vector space; combined with domain knowledge and relationship weights provided by the knowledge graph, a comprehensive matching score is calculated; (3) Hybrid recommendation system: It integrates collaborative filtering signals and content-based matching scores, and may introduce deep learning ranking models (such as Learning to Rank-LTR) to rank candidate matching results more accurately; LTR models (such as LambdaMART) can learn which matching features (such as core skills matching degree, years of experience, and company culture fit) have a greater impact on the final "successful recruitment" result; (4) Personalization and context awareness: Consider the user's real-time behavior (currently viewed job type, search keywords), the urgency of the company's recruitment, the job search activity of talents, geographical location preferences and other contextual information, and dynamically adjust the recommendation results.

[0032] 4. Big Data Processing and Cloud Computing: (1) Data lake / warehouse: Use Hadoop, Spark, Flink and other technologies to process massive amounts of structured and unstructured data (resumes, job descriptions, behavior logs, enterprise information); (2) Real-time computing: Use Kafka, Storm, Spark Streaming and other technologies to process real-time user behavior data and realize the instant update of recommendation results; (3) Cloud-native deployment: Relying on cloud platforms such as AWS / Azure / GCP, we realize the elastic scaling of computing resources (Kubernetes containerization) to ensure performance and stability under high concurrency access; and use distributed databases (such as Cassandra, MongoDB) and vector databases (such as Milvus, Pinecone) to efficiently store and retrieve vector data.

[0033] 5. Personalized Interaction Intelligent Search: The search box not only supports keywords, but also understands natural language queries (such as "finding a digital marketing manager with more than 3 years of experience in the Southeast Asian market") and returns intelligently sorted results; Personalized recommendation feed: Display "You May Also Like" and "Highly Matching Jobs / Talents" lists on the homepage or in key locations, dynamically updated based on user profiles and behavior; Intelligent Matching Report: On the talent details page or job details page, it intuitively displays the matching degree analysis between the talent / job and the current user's needs (skill radar chart, experience fit, potential assessment, etc.). Intelligent reminders and push notifications: Based on the matching algorithm, push notifications to talents about "newly launched matching positions" and to HR about "newly emerging high-quality candidates"; Feedback mechanism: Provide convenient feedback channels such as "not interested" and "reasons" (e.g., salary mismatch, location mismatch, skill mismatch) for algorithm learning and optimization.

[0034] The operational process (end-to-end) is as follows: Step 1: Data Input and Preprocessing: On the talent side: Users upload resumes (or fill in structured information), set job preferences (position, industry, salary, location, etc.), and generate behavioral data (browsing, applying, saving); On the enterprise side: HR publishes job descriptions (JD), sets screening criteria (hard / soft requirements, priorities), and generates behavioral data (viewing resumes, initiating communication / interviews, and making hiring decisions); System: Collects, cleans, and standardizes the above data in real time, and stores it in a data lake.

[0035] Step 2: Image Construction and Vectorization: The NLP engine parses resumes and job descriptions (JDs) to extract key information; it combines knowledge graphs to perform semantic enhancement and association expansion (e.g., recognizing "proficient in TensorFlow" means having "deep learning" and "machine learning" skills); and generates dynamically updated talent profile vectors and job profile vectors, which are then stored in a vector database.

[0036] Step 3: Matching Calculation and Recommendation Generation Trigger: Matching calculations are triggered when talents browse job postings, companies search for resumes, new job postings are published, new resumes are uploaded, or system scheduled tasks are completed. Recall: Use an efficient approximate nearest neighbor search algorithm to quickly retrieve the Top N candidates (jobs or talents) most relevant to the target (talent or job) from the vector database. This step ensures efficiency. Fine ranking: The retrieved results are finely ranked using a more complex LTR model. The model comprehensively considers: semantic matching degree based on content (vector similarity); collaborative filtering signals (preferences of similar users / enterprises); relevance of knowledge graph reasoning; user personalized preferences and contextual information; business rules (such as hard condition filtering). Generate a recommendation list: Output recommendation results sorted by matching degree, and provide reasons for the recommendation (such as "The 'cross-border e-commerce platform operation' required for this position is highly matched with your 'Amazon store management' experience in your resume", "Your 'Python data analysis' skills are the core skills for this position"), to enhance user trust and decision-making basis; Algorithm design must be wary of and mitigate recommendation biases caused by historical data biases (such as gender, school, and regional discrimination). Mechanisms such as bias correction techniques, fairness constraints, and multi-dimensional evaluation should be employed to promote fairer matching.

[0037] Step 4: Results Presentation and Feedback Loop Wiser App / Web for talent: The “Recommended Jobs” section displays a list of matching job positions, highlighting key matching points and reasons for recommendation (explainable AI). Talents can apply, save, or ignore these positions. Wiser management backend for enterprises: In the "Recommended Talents" list or talent search for the corresponding job, a list of matching candidates is displayed, with markings such as matching degree, key skill fit points, and potential assessment. HR can initiate communication, arrange interviews, and record feedback. Feedback collection: The system continuously collects explicit feedback from users on the recommendation results (submission, favorite, ignore, interview results, hiring results) and implicit feedback (browsing time, scrolling depth). Model optimization: Feedback data is fed back into the training set for continuous training and optimization of NLP models, recommendation models (especially LTR), and knowledge graphs, forming a closed loop of "data → model → recommendation → feedback → optimization".

[0038] II. Interview-Assessment: A multi-dimensional, comprehensive, and objective evaluation is conducted based on the overall interview process (including five major steps: competency selection, initial questioning, answer quality assessment, intelligent follow-up questioning, and intelligent evaluation). This includes the following techniques: 1. Multimodal Analysis Engine ASR and sentiment analysis: Using deep learning models (such as Transformer-based architecture) to translate candidate speech in real time, and combining voiceprint features to analyze speech rate and pause frequency to identify tension or confidence. Computer vision (CV) technology: Captures micro-expressions (such as upturned corners of the mouth / slightly furrowed eyebrows) and eye focus through facial key point detection (such as OpenFace), and analyzes cooperation intentions by combining body movements; Natural Language Processing (NLP): Based on BERT or GPT variants, analyze the semantic logic and keyword coverage of responses to identify the completeness of the STAR method (context-task-action-outcome).

[0039] 2. Dynamic Question Bank and Intelligent Interactive System Knowledge graph-driven question bank generation: Integrating a knowledge graph of the digital trade field (including industry terminology and job skill trees), it automatically generates contextualized questions based on competency models (such as communication skills and cross-border operation capabilities); for example, for the "cross-border e-commerce operation" position, it generates "How to handle logistics delay disputes in the Southeast Asian market?". Real-time follow-up questioning mechanism: If the candidate's answer is vague (such as "I solved the problem"), the system will initiate follow-up questions based on gap identification: "Please explain in detail the steps you took to coordinate with the logistics supplier?", and use reinforcement learning to optimize the follow-up questioning strategy.

[0040] 3. Three-tier evaluation model and decision-making system Surface analysis: grammatical correctness and accuracy of terminology usage (NLP part-of-speech tagging); Mid-level analysis: logical coherence and case authenticity (verifying the rationality of industry data through knowledge graphs); Deep analysis: potential assessment (such as stress resistance and innovative thinking), combined with psychometric models to output personality profiles; Matching score calculation: Matching score = (Skill compatibility × 0.4) + (Cultural compatibility × 0.3) + (Potential value × 0.3).

[0041] 4. Data Processing and Model Training Feature engineering: Extract 500+ dimensional features (such as semantic word frequency and facial expression change frequency) from interview videos, and input them into the model after PCA dimensionality reduction; Model fusion architecture: Base layer: XGBoost processes structured features (such as response time); Enhancement layer: Graph Neural Network (GNN) models skill correlations; Continuous learning mechanism: The evaluation weights are optimized by back-tracking the performance data of candidates after they are hired (e.g., model calibration is triggered by "high innovation score but low performance").

[0042] Specific operating procedures Step 1: Competency Binding and Question Generation Enterprises select competency tags such as "cross-cultural communication" and "crisis management" → the system calls the knowledge graph to generate behavioral event interview (BEI) questions; for example, if "data analysis capability" is selected, the system will push "Please analyze a case of optimizing ROI through data".

[0043] Step 2: Initial Questions and Real-time Analysis During the candidate's video response, the system simultaneously performs: speech-to-text conversion + emotional polarity scoring (e.g., positive words accounting for ≥60% → +5 points); micro-expression analysis: frequent lip pursing → indicating an increase in tension level.

[0044] Step 3: Intelligent follow-up questions and in-depth analysis If the answer does not mention specific action steps, trigger a chain of follow-up questions: "What specific tools are involved in the strategy you mentioned?" → "How are team disagreements resolved?"; Use a decision tree model to select the follow-up questioning strategy based on the type of missing keywords.

[0045] Step 4: Generation of the three-tier assessment report Surface report: Speech clarity 90%, technical terms used 7 times; Mid-level report: Case authenticity verification passed (matching customs export data fluctuations); Deep-level report: Stress resistance level A (heart rate fluctuation <10% during crisis response); Output radar chart and improvement suggestions: "Need to strengthen cross-border payment solution design capabilities".

[0046] Wiser's matching and scheduling engine, through semantic penetration matching and a federated learning ecosystem, achieves efficient coupling between the talent supply side and the demand side of thousands of enterprises. Specifically, it includes the following technologies: 1. Multimodal Talent Profiling Engine Dynamic data fusion: By analyzing user resumes (education / skills / project experience) through NLP models and combining behavioral data from interview scenarios (micro-expressions, voice sentiment analysis), a multi-dimensional vector profile is generated, including hard skills (mastery of the technology stack), soft skills (communication skills), and career orientation (MBTI personality). Knowledge graph association: By connecting to the human resources ontology database, user profiles are dynamically associated with job competency models and industry demand heatmaps.

[0047] 2. Job Matching System for 1,000 Enterprises Intelligent routing algorithm: Initial screening layer: Based on the rule engine, filter hard conditions (such as education / certificate requirements) to eliminate unmatched positions; Calculation layer: Use multimodal contrastive learning (CLIP improved version) to calculate the semantic matching degree between talent profile and enterprise job description (JD) and output a competency score of 0-100. Real-time demand pool: Integrates APIs from recruitment platforms across the internet (such as Boss Zhipin and Liepin) to build a job demand heatmap and dynamically updates corporate hiring priorities; Cross-platform deduplication mechanism: Avoid duplicate recommendations from subsidiaries within the same group by using a unique enterprise coding library; Culture fit algorithm: Combining Holland's theory of occupational interests, it recommends positions that are highly compatible with corporate culture (such as "innovative team" matching "highly pioneering personality").

[0048] 3. Federated Learning Privacy Computation Framework While protecting user privacy, distributed model training is used: user data is encrypted locally, and only feature vectors are shared to the central server; after the enterprise provides feedback on the hiring results, the matching model weights are updated in reverse to improve long-term accuracy. Dynamic threshold control: Introducing a GBDT gating network to automatically adjust the matching threshold based on the submission feedback rate (if the initial matching rate is low, the weight of educational requirements will be relaxed).

[0049] Specific operating procedures Step 1: One-click submission and profile generation After a user uploads their resume, the system automatically: extracts key entities (such as "3 years of experience in cross-border e-commerce operations + IELTS 7.5"); and activates the AI ​​interview module to supplement implicit ability assessments (such as stress resistance being quantified through stress test questions).

[0050] Step 2: Intelligent Routing and Dynamic Matching Demand pool penetration: Based on the urgency of job openings (such as the "urgent hiring" tag) and corporate talent preferences (historical data on similar talent recruitment), matching companies are ranked and sorted. Two-way negotiation mechanism: If the user's expected salary is greater than the company's budget, the system will automatically compromise and recommend positions with overlapping "salary flexibility ranges".

[0051] Step 3: Real-time feedback and closed-loop optimization Progress tracking panel: Visually displays the application status (e.g., "50 applications submitted → 10 companies viewing resume → 3 companies initiating interviews"). Reinforcement learning iteration: After a user rejects a certain type of job, the recommendation weight of the same type is automatically reduced (e.g., if a user frequently rejects "cross-border live streaming job", the recommendation will switch to "overseas market planning").

[0052] Matcher's candidate matching system, geared towards enterprises, helps them automatically match the most suitable candidates based on skills, experience, and job requirements. It offers search and filtering functions, allowing companies to quickly find specific types of talent. Clicking on a candidate's profile page allows users to communicate, add them to the candidate pool, request to view interview videos, and see their talent profile. Specifically, it includes the following technologies: 1. Multimodal Job Requirements Analysis Engine Semantic understanding technology: The system uses a variant of the BERT model to parse job descriptions (JDs) input by companies and extract key elements (such as skill requirements, industry experience, and salary range). For example, if the input is "Requires 3 years of cross-border live streaming experience, proficient in TikTok operations", the system will automatically mark it as [Job = Cross-border Live Streaming Operations, Years = 3 years, Core Skills = TikTok Operations]; Dynamic knowledge graph assistance: Integrating human resource ontology with digital trade industry graphs (such as RCEP policy terms and regional development indices) to identify implicit needs; for example, "familiar with the Southeast Asian market" automatically links to RCEP tariff rules and local consumption data; Global digital trade talent data: such as ASEAN market-specific talent tags, to analyze regional skills gaps in real time.

[0053] 2. Real-time talent profile matching system Multi-source data fusion: Connect with user profiles generated by Wiser (including competency models for AI interview assessments, MBTI personality analysis, etc.), and combine them with publicly available data (salary heatmaps from recruitment websites, industry trend reports) to construct a dynamic talent vector; Matching Algorithms: Content-based matching: The CLIP improved model is used to calculate the cosine similarity between talent skill vectors and job requirements; Collaborative filtering: The historical recruitment behavior of enterprises (such as hiring preferences) is analyzed, and the weight allocation is optimized through the LightGBM model.

[0054] 3. Hybrid Expert (MoE) Decision Engine Expert pool design: We employ job compliance experts to verify whether job descriptions (JDs) comply with regional policies through rule engines and policy NLP analysis; we employ cost optimization experts to predict the relationship between salary and talent retention rate through survival analysis models (Cox proportional hazards); and we employ potential assessment experts to analyze talent growth curves through time series forecasting (Prophet). Gated network: The GBDT model is used to dynamically allocate expert weights (e.g., if the company requires "low-cost, high-potential talent", the weight of the cost optimization expert is increased to 0.7).

[0055] The operation process is as follows: Step 1: Intelligent Requirement Analysis and Enhancement Input: Job description (JD) posted by the company (e.g., "Cross-border E-commerce Operations Manager, fluent in English, familiar with the EU market"). Enhanced parsing: NLP model identifies core entities [language = English, region = EU, job type = e-commerce operations]; knowledge graph supplements implicit requirements: knowledge of EU Digital Markets Act compliance, and equivalent standard of IELTS 6.5+ in English.

[0056] Step 2: Dynamic matching of talent pool Recall phase: Quickly retrieve the Top 100 matching talents from vector databases (such as Milvus), and prioritize those with English proficiency ≥ IELTS 6.5 (Wiser assessment data) and experience in processing EU cross-border orders (historical transaction records). Fine-tuning stage: Multi-expert collaborative scoring: Cost experts assess the reasonableness of salary expectations, and potential experts analyze the stability of career trajectories; output a matching report (e.g., "Candidate A matching degree 92%, core strength: EU order growth rate 35%").

[0057] Step 3: Continuous Optimization and Feedback Enterprise behavior tracking: Record HR's click / ignore behavior when recommending candidates, and optimize matching strategies through reinforcement learning (PPO algorithm); Data closed loop: The knowledge graph is updated monthly (e.g., new policies on live-streaming e-commerce in Southeast Asia are added), and expert weights are adjusted through meta-learning.

[0058] The Matcher-based AI initial interview setup system allows companies to click "Create Interviewer," input creation characteristics, and create their own interviewer. They can then select the appropriate interviewer to conduct AI interviews for different positions. The AI ​​interviewer automatically conducts initial interviews with candidates and generates standardized reports. Specifically, it includes the following technologies: 1. Virtual Interviewer Generation System Digital human-driven technology: Virtual avatars are constructed using 3D modeling and dynamic rendering technologies (such as Blender / Unity), and human-like interaction is achieved through a multimodal emotion synthesis engine (voice + micro-expression); JD.com's AI interview agent provides 24-hour online service through digital humans and supports the recognition of more than 20 dialects; Large-scale dialogue engine: Generates interview dialogues based on large language models such as ChatGLM, and dynamically adjusts questioning strategies by combining job knowledge graphs; for example, the Yonyou Dayi platform uses NLP to parse job description (JD) requirements and automatically generates contextualized questions.

[0059] 2. Hybrid Expert Model (MoE) Gated network: Expert weights are assigned based on job type (e.g., for technical positions, the weight of "Code Ability Expert" is 0.7). Expert Pool: Behavioral Event Analysis Experts: LSTM+CRF model to assess the completeness of the STAR principle (Context-Task-Action-Outcome); Potential Prediction Experts: Integrating Holland's theory of career interests to extrapolate long-term career trajectories.

[0060] 3. Intelligent Question Bank and Question Generation Competency model driven: Construct a job competency matrix based on iceberg theory / MBTI, for example, JD.com has established a question bank covering 20 core competencies, and HR can customize the scoring weights; Dynamic problem generation algorithm: Initial question: Extract key experiences through resume parsing (BERT variant) to generate behavioral interview questions (such as "Describe the challenges of a project and how you dealt with them"). Intelligent follow-up questions: Employs an intent recognition model (LSTM + attention mechanism) to trigger in-depth follow-up questions based on keywords in the answer; for example, if a candidate mentions "team conflict", it automatically asks for specific steps to resolve the issue.

[0061] 4. Multimodal evaluation system Three-tier analysis framework: At the content layer, NLP semantic analysis is used to output skill matching degree; at the expression layer, voice sentiment analysis is used to output communication ability score; at the behavior layer, CV micro-expression detection is used to output stress resistance assessment. Anti-cheating mechanisms: real-time face comparison + screen switching detection + voiceprint verification to ensure the authenticity of the interview.

[0062] 5. Evaluation and Feedback Engine Chain-of-Thought technology: automatically extracts the chain of evidence in the answer to generate a scoring report. For example, when the JD.com system outputs the conclusion of "insufficient logic", it marks the specific sentence positions. Personality prediction model: Integrating Holland's theory of occupational interests, it predicts MBTI type by answering text (e.g., frequent use of "we" may indicate an extroverted personality). Real-time feedback optimization: Reinforcement learning (PPO algorithm) simulates corporate hiring decisions and dynamically adjusts questioning strategies (e.g., if three consecutive candidates score low on algorithm questions, the difficulty is reduced); A / B test question bank update mechanism: Eliminate low-discrimination questions (e.g., 90% of candidates get full marks).

[0063] The process for creating an AI interviewer system is as follows: Step 1: Defining Roles and Tasks The role of the interviewer is clearly defined through a rules engine (e.g., asking questions for assessment, not answering questions). Enter the job requirements (e.g., "technical position in the computer industry"), and the system will automatically generate an interview logic tree, including a competency model (communication skills, professional skills, etc.) and taboo rules (e.g., prohibiting subjective bias).

[0064] Step 2: Multimodal Analysis Engine Integration Automatic speech recognition (ASR): Translate candidate responses in real time, analyze speech rate and pause frequency to assess stress resistance; Computer vision (CV): Capturing micro-expressions and recognizing emotional stability through facial landmark detection (such as OpenFace); Natural Language Processing (NLP): Combining BERT variants to parse the logic of the answers and matching them with a job keyword database (e.g., "cross-border e-commerce operations" requires frequent mentions of "data analysis" and "user growth").

[0065] Step 3: Building a Dynamic Knowledge Base Integrating psychological models (MBTI, iceberg theory) to generate a personality fit report; Access industry databases (such as question banks from 300+ major companies) and build differentiated question banks based on job type: Technical positions: focus on algorithm questions (such as LeetCode problems) and system design; Marketing positions: introduce scenario-based simulation questions (such as "How to plan a cross-border live stream"). (1) Technical positions (such as software development) Initial questions: Parse the technology stack in the resume (such as Python / React) and automatically generate targeted questions (e.g., "Explain the principle of React's virtual DOM"). Intelligent follow-up questions: If the answer mentions "performance optimization", it will trigger in-depth follow-up questions (e.g., "How to quantify the optimization effect?"). Evaluation dimensions: The coding practice questions are run in real time in a sandbox environment to verify correctness; communication skills are scored based on the coherence of the sentences (e.g., points are deducted for more than 3 pauses). (2) Cross-border operations positions Scenario simulation test: Play a video of a conflict case in the Southeast Asian market, ask participants to verbally describe solutions, and assess cultural adaptability; Competency matching: Use the CLIP improved model to compare the responses with job description (JD) keywords (such as "RCEP policy" and "multi-platform operation") and output the matching percentage; (3) Financial positions Risk warning mechanism: The survival analysis model (Cox model) detects suspicious points in the resume (such as frequent job hopping) and generates verification questions (e.g., "Explain why the previous job lasted only 5 months"). Compliance screening: NLP scans for sensitive words (such as "insider trading") in responses and automatically marks them as risky.

[0066] like Figure 2 As shown, a talent and job intelligent matching decision-making method for flexible employment is presented, with the following specific steps: Step 1: Data Fusion and Profile Building (Matching Foundation); Step 1.1: Multimodal Data Acquisition On the talent side: analyze user information to analyze career planning, analyze historical AI interview videos to obtain evaluations, skills certificates (OCR recognition), and platform behavior patterns (click / search / application records); On the enterprise side: Deconstruct the job description (JD), job requirements (responsibilities / skills / experience levels), enterprise salary range, and team culture tags (NLP sentiment analysis); Technical support: BERT variant semantic parsing, distributed crawler system, OAuth authorization to obtain third-party data (such as salary databases of recruitment websites); Step 1.2: Dynamic Image Generation Talent profiling: Transforming skills, experience, and behavioral preferences into high-dimensional vectors (such as Word2Vec embedding), and overlaying them with psychological assessment results (MBTI / Holland Occupational Interests) to form a 360° view; Job profile: Based on knowledge graphs, skill nodes are associated (e.g., "Python programming → Machine learning engineer job weight 0.8"), and implicit requirements are quantified (e.g., "stress resistance = overtime frequency × project urgency"). Technical support: Graph Neural Network (GNN) for modeling correlations, and Vector Database (Milvus / Pinecone) for storing profiles.

[0067] Step 2: Intelligent Matching Engine (Algorithm Core) Step 2.1: Through intelligent talent-job precise matching and recommendation, an efficient, accurate and dynamic matching bridge is established between massive talent resumes and job requirements. By going beyond traditional keyword matching, a deep understanding of the fit between talent's abilities, experience, potential and job requirements is achieved, realizing personalized and intelligent two-way recommendations. Step 2.2: One-click matching and scheduling to achieve efficient coupling between talent supply and demand from thousands of enterprises; Step 2.3: Job candidate matching. Based on skills, experience and job requirements, candidates are matched to help companies find specific types of talent. The AI ​​interviewers created by the company for different positions conduct the initial interviews with the candidates.

[0068] Step 3: Interactive Verification and Iteration (Effect Enhancement) Step 3.1: AI Two-Way Interactive Verification On the talent side: AI interview platforms automatically ask follow-up questions (such as "Please give an example of how to improve the conversion rate of the live broadcast room") to verify the authenticity of skills; For enterprises: The intelligent matching report visually presents the matching criteria (such as skills radar chart and cultural compatibility heat map); Step 3.2: Iteration driven by negative feedback Collect failure case studies: reasons why candidates refuse interviews, and reasons why companies dismiss candidates (such as "skills overload but salary mismatch"). Incremental training of the model: Fine-tuning the BERT semantic understanding layer and LTR weights weekly with new data.

[0069] Step 4: End-to-end traceability and fairness assurance (trustworthy mechanism) Step 4.1: Explainability Transparency Deconstruct matching decisions using SHAP values ​​(e.g., "This position is recommended due to an 82% skill match rate and a 15% bonus for companies in policy-benefit areas"). Step 4.2: Bias Elimination Techniques Adversarial training: Add debiasing constraints (such as gender / school attribute obfuscation) to the feature extraction layer. Fairness verification: A / B testing to compare the differences in interview pass rates for different types of talent.

[0070] like Figure 3As shown, an electronic device includes: at least one processor; and memory that is communicatively connected to at least one processor; The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to execute a talent and job intelligent matching decision-making method.

[0071] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A talent and job intelligent matching decision-making method for flexible employment, characterized in that, The specific steps are as follows: Data fusion and profile building phase: multimodal data collection, parsing user information to analyze career planning needs, parsing historical AI interview videos to obtain evaluations; breaking down job descriptions and job requirements, and enterprise salary bandwidth; Dynamic profile generation transforms skills, experience, and behavioral preferences into high-dimensional vectors, overlays psychological assessment results to form a 360° view, and generates a talent profile. Based on knowledge graph-related skill nodes, implicit requirements are quantified to generate job profiles; Intelligent matching engine stage: Through intelligent talent-job precise matching and recommendation, a matching bridge is established between talent resumes and job requirements; one-click matching and scheduling realizes the coupling of talent supply side and thousands of enterprise demand side; job candidate matching is based on skills, experience and job requirements, and enterprises create AI interviewers for different positions to conduct initial interviews with candidates. Interactive verification and iteration phase: AI two-way interactive verification, AI interview platform automatically asks follow-up questions to verify the authenticity of skills; negative feedback-driven iteration, collecting failure cases, including reasons for talent rejection and reasons for company dismissal; fine-tuning BERT semantic understanding layer and LTR weights with new data every week; End-to-end traceability and fairness assurance phase: Deconstructing matching decisions using SHAP values; Add bias removal constraints to the feature extraction layer; use A / B testing to compare the differences in interview pass rates for different types of talent.

2. The intelligent talent and job matching decision-making method for flexible employment according to claim 1, characterized in that: The specific process of career planning needs analysis is as follows: Demand analysis and data fusion: After the user inputs their requirements, the NLU model extracts key entities, crawls the high-frequency skill requirements in the job description of the target position, and retrieves the user's historical performance data. MoE in-depth analysis, construction of SWOT matrix, and opportunity prediction; The system generates dynamic paths, compares the user's current skill vector with the target job vector, identifies key gaps, analyzes capability deficiencies, and ultimately generates solutions.

3. The intelligent matching decision-making method for talent and positions for flexible employment as described in claim 1, characterized in that: The specific process of the AI ​​interview-assessment is as follows: Competency binding and question generation: After an enterprise selects competency tags, the system calls the knowledge graph to generate behavioral event interview questions; Initial Questioning and Real-Time Analysis: While candidates are answering questions via video, the system simultaneously performs speech-to-text conversion, sentiment polarity scoring, and micro-expression analysis. Intelligent follow-up questioning and in-depth analysis: If the answer does not mention specific action steps, a follow-up questioning chain is triggered; a decision tree model is used to select the follow-up questioning strategy based on the type of missing keywords; Three-tiered assessment report generation: A surface-level report assesses speech clarity and the use of technical terminology; a mid-level report assesses whether the case has passed authenticity verification. Generate an in-depth report to assess resilience; finally, output a radar chart and improvement suggestions.

4. The intelligent talent and job matching decision-making method for flexible employment according to claim 1, characterized in that: The specific process of intelligent talent-job matching and recommendation is as follows: Data input and preprocessing: Users upload resumes, set job preferences, and generate behavioral data; HR publishes job descriptions, sets screening criteria, and generates behavioral data. Profile building and vectorization: The NLP engine parses resumes and job descriptions to extract key information; it combines knowledge graphs to perform semantic enhancement and association expansion; and it generates dynamically updated talent profile vectors and job profile vectors, which are then stored in a vector database. Matching calculation and recommendation generation: The most relevant candidates to the target are quickly retrieved from the vector database using an efficient approximate nearest neighbor search algorithm; the recalled results are finely sorted using a more complex LTR model; finally, the recommendation results are output in order of matching degree, and the reasons for the recommendation are provided. Results Presentation and Feedback Loop: On the talent side, the "Recommended Jobs" section displays a list of matching positions, annotating key matching points and reasons for recommendation, allowing users to apply, save, or ignore. On the enterprise side, the "Recommended Talents" list corresponding to the position or the talent search displays a list of matching candidates, annotating the matching degree, key skill fit, and potential assessment, allowing users to initiate communication, arrange interviews, and record feedback. The system continuously collects explicit and implicit user feedback on the recommendation results, and the feedback data is fed back into the training set for continuous training and optimization of the NLP model, recommendation model, and knowledge graph.

5. The intelligent talent and job matching decision-making method for flexible employment according to claim 4, characterized in that: The matching calculation and recommendation generation are used to trigger the matching calculation when talents browse job postings, companies search for resumes, new job postings are published, new resumes are uploaded, or system scheduled tasks are performed.

6. The intelligent talent and job matching decision-making method for flexible employment according to claim 4, characterized in that: The matching calculation and recommendation generation are comprehensively considered by the recommendation model, including: semantic matching degree based on content, collaborative filtering signals, relevance of knowledge graph reasoning, user personalized preferences and contextual information, and business rules.

7. The intelligent matching decision-making method for talent and positions for flexible employment according to claim 1, characterized in that: The specific process of one-click matching and scheduling is as follows: One-click application and profile generation: After users upload their resumes, the system automatically extracts key entities; the AI ​​interview module is activated to supplement implicit ability assessment; Intelligent routing and dynamic matching: Demand pool penetration, ranking and classifying matching companies based on job urgency and corporate talent preferences; Two-way negotiation mechanism, the system automatically compromises and recommends overlapping positions; Real-time feedback and closed-loop optimization: Visual display of application status; automatic reduction of recommendation weight for similar positions after a user rejects a certain type of job.

8. The intelligent talent and job matching decision-making method for flexible employment according to claim 1, characterized in that: The specific process of setting up the AI ​​initial interview is as follows: Role and task definition: The rules engine clarifies the interviewer's responsibilities. By inputting job requirements, the system automatically generates an interview logic tree, including a competency model and taboo rules. Multimodal analysis engine integration: speech recognition translates candidate answers in real time, analyzes speech rate and pause frequency to assess stress resistance; computer vision captures micro-expressions through facial key point detection to identify emotional stability; natural language processing combined with BERT variants parses the logic of answers and matches them with a job keyword database; Dynamic knowledge base construction: Integrating psychological models to generate personality fit reports; Access industry databases and build differentiated question banks based on job type.

9. A talent and job intelligent matching system for flexible employment, used to execute the talent and job intelligent matching decision-making method as described in any one of claims 1-8, comprising a Wiser end and a Matcher end, characterized in that: Wiser includes a career planning analysis system, an AI interview platform, and a matching and scheduling engine. The career planning analysis system is based on a hybrid expert model analysis engine, integrating NLU model, dynamic profiling engine and dynamic knowledge base system. By combining user information, it finds a suitable path and realizes career assessment, job analysis, ability assessment, resume optimization and workplace Q&A functions. The AI ​​interview platform is based on the intelligent talent-job precise matching and recommendation function, which establishes a matching bridge between massive talent resumes and job requirements. Based on the interview-assessment function, it combines multimodal analysis and dynamic question bank to conduct multidimensional, comprehensive and objective evaluation. The matching and scheduling engine can deliver to multiple enterprises in one go, relying on dynamic talent profiles and intelligent matching with job requirements; The Matcher client has a candidate matching system and an AI-powered initial interview setup system. The candidate matching system integrates a multimodal job requirement analysis engine, a real-time talent profile matching system, and a hybrid expert decision engine. It matches candidates based on skills, experience, and job requirements, and provides search and filtering functions to help companies find specific types of talent. The AI-powered initial interview setup system is based on a virtual interviewer generation system and an intelligent question bank and question generation system. It helps companies create an interviewer and select the appropriate interviewer to conduct AI interviews for different positions.

10. An electronic device, characterized in that: The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the talent and job intelligent matching decision method as described in any one of claims 1 to 8.

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